To our knowledge, the first work to present a deep learning approach for SISR is [1]}. Dong et al. [1]} proposed a CNN composed of 8 convolutional layers. The network was trained in an end-to-end fashion, minimizing the reconstruction error between the HR image and the output of the network. They used bicubic interpolation to resize the image, before giving it as input to the network. Hence, the CNN takes a blurred HR image as input and learns how to make it sharper. Since the input is an HR image, this type of CNN is time consuming. Therefore, Shi et al. [3]} introduced a new method for upsampling the image using the CNN activation maps produced by the last layer. Their network is more efficient, because it builds the HR image only at the very end. Other works, such as [4]}, proposed deeper architectures, focusing strictly on accuracy. Indeed, Zhang et al. [4]} presented one of the deepest CNNs used for SR, composed of 400 layers. They used a channel attention mechanism and residual blocks to handle the depth of the network.
